A More Accurate Algorithm Comparison through A/B Testing using Offline Evaluation Methods
A/B testing is the gold standard for selecting the better algorithm in online services. While offline evaluation has attracted attention as a safer alternative due to the high experimental costs and the potential risk of degrading user experience and revenue in A/B testing, it is widely recognized that the estimation accuracy of offline evaluation is substantially lower. As a result, final selection decisions are typically made through A/B testing. Contrary to this conventional view, we reveal a counterintuitive phenomenon in which A/B testing can produce a higher algorithm selection error rate than offline evaluation. This occurs because the sample mean estimator used in A/B testing does...
| Authors | Koki Konishi, Masataka Ushiku, Yuta Saito |
|---|---|
| Primary category | cs.LG |
| Published | 2 July 2026 |
| Last updated at source | 5 August 2026 |
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